Conventional radiography uses photostimulable phosphor screens and photosensitive silver-halide film media for recording images of human anatomy. CAD/CADx algorithms have been developed to assist radiologists and physicians to detect and diagnose various diseases. Before digital radiography (DR) systems became widely available, a considerable amount of research and development effort had been expended, over a period of about twenty years, to train and optimize CAD/CADx algorithms based on film images digitized with film digitizers.
Advances in imaging technology now make it possible to capture radiography images directly as digital data, without the use of photosensitive film. Digital imaging can be performed using computed radiography (CR) apparatus that records image data on an erasable sheet of stimulable storage phosphors and using direct DR apparatus that obtains image data directly from received radiation. In this specification, CR and DR collectively are referred to as digital radiography imaging systems. Digital receivers of these types are particularly advantaged, not only for their wider dynamic range over conventional screen/film imaging, but also because they create new opportunities to separate and individually optimize image capture, processing, and display processes of the overall imaging chain.
Although digital radiography imaging systems enjoy advantages over conventional screen/film-based systems, they impose a new challenge to the existing base of CAD/CADx algorithms. As is well known to those skilled in the diagnostic imaging arts, screen/film, CR, and DR imaging modalities exhibit very different response characteristics with respect to radiation intensity, image sharpness, system noise, and other factors. CAD/CADx algorithms that are particularly trained and optimized for use with digitized data from screen/film systems may not perform well when provided with raw (unprocessed) data from the digital radiography imaging system.
FIG. 1 shows a typical response of the screen/film system between the incident x-ray exposure level (E) and the film density (D), which generally follows a sigmoid shape between D and the logarithm of E. Digital radiography systems, on the other hand, generally record the x-ray exposure level in linear exposure space, then optionally convert this into logarithmic exposure space.
Following the pattern used for the Digital Imaging and Communications in Medicine (DICOM) standard, digital radiography imaging systems output the image data in either of two main formats. These formats differ from each other in function and in data representation and are appropriately termed “For Processing” and “For Presentation” formats. Referring to the block diagram of FIG. 2, raw image data from a digital detector 10 is conditioned by a processor 12 that provides either or both types of output, either For Presentation data for a display 14 or data For Processing by a CAD system 20 or other image processing systems.
For Presentation data are used for input to film printers or diagnostic workstations, so that the displayed films on a light-box or the displayed images on a diagnostic workstation would be directly suitable for visual assessment by radiology personnel for diagnosis. A number of methods were initially devised for improving the appearance of images obtained from CR or DR digital detectors, so that the digital image resembles and improves the corresponding film image. One example technique for improvement of the presentation image is described in commonly assigned U.S. Pat. No. 6,778,691 entitled “Method of Automatically Determining Tone-Scale Parameters for a Digital Image”. As radiologists become more familiar with the digital “look” created by digital image processing, their preferences gradually shift toward images with additional spatial processing such as edge sharpening (U.S. Pat. No. 5,369,572) and dynamic range compression (U.S. Pat. No. 5,317,427). Because For Presentation data are optimized for display and visual assessment, edge characteristics associated with various disease features in the image can be artificially modified by the image processing algorithms that improve visual contrast enhancement (for example, spatial processing). However, because of this modification, this same For Presentation data can be unsuitable for the CAD/CADx algorithms that have been trained based on digitized film images.
For Processing data overcome the problem associated with the For Presentation data and are intended for applications such as CAD/CADx. For Processing data is usually the unprocessed raw data of the digital radiography imaging systems, that is, the original image data provided either in linear exposure space or in logarithmic exposure space. However, in practice, such data may not be directly usable by the CAD/CADx algorithms or may yield poor algorithm performance. The digital data needs some amount of preprocessing in order to compensate for differences in imaging characteristics between screen/film systems and digital radiography imaging systems in terms of exposure response, sharpness, noise, and other characteristics.
There is, then a need for data conversion methods for CAD/CADx that allow digital radiography data to more accurately emulate digitized data received from scanned films.